Users of many online products face a plethora of recommendations in the form of similar products—e.g. “People also viewed” type recommendations, as well as suggested content personalized for them—e.g. “Based on your preferences” type recommendations. Users trust recommender systems more to the extent that they believe the content is tailored to them. This is especially important when the context in which a recommendation is made is vague. For example, on product pages the context for making “People also viewed” recommendations is clear—it is people who also viewed the product a user is looking at. However, when an online system makes general recommendations outside of a specific context, a lack of clarity can decrease a user's trust in the recommendation.